Recent studies have suggested that rainwater infiltrates not only the soil layer but also the underlying bedrock in small mountainous catchments, forming a bedrock aquifer. This bedrock groundwater subsequently discharges into the soil layer, potentially affecting the initiation of shallow landslides. To clarify this influence, it is essential to understand the runoff dynamics of bedrock springs in response to precipitation. However, direct observation of bedrock spring runoff remains challenging because bedrock springs are usually covered by thick soil layers. As a result, only a limited number of studies have investigated the runoff characteristics of individual bedrock springs, and the development of runoff models for bedrock springs is still insufficient. In this study, we conducted detailed field observations in a small mountainous catchment (6.87 ha) in Hokkaido, northern Japan. The study site is underlain by granite, and bedrock layer is exposed on both banks of the stream, where springs emerge from fractures in the bedrock. As of 2025, multiple bedrock spring outlets have been identified within the catchment. Soil temperature, bedrock spring water temperature, precipitation, and bedrock spring runoff were monitored. Soil temperature and bedrock spring water temperature were continuously recorded at hourly intervals. Precipitation was measured at hourly intervals using a tipping bucket rain gauge. Bedrock spring runoff was measured by constructing small dams immediately downstream of each spring outlet and directing all spring water into triangular weirs or tipping bucket discharge gauges. In addition, soil water, bedrock spring water, and rainwater were collected for water quality analysis. Soil temperature, bedrock spring water temperature, and water quality data were used to estimate the origin of the bedrock spring water. We applied the Pw1 model, a functional model based on antecedent precipitation, to reproduce bedrock spring runoff dynamics. This model was originally proposed by Kosugi et al. (2013) to reproduce groundwater level variations that cause deep-seated landslides, using antecedent precipitation with an arbitrary half-life time and positive constants. Model parameters were optimized to maximize the Nash–Sutcliffe efficiency (NSE). Finally, we discuss the relationship between the origin of bedrock spring water and the model parameters.

Reference
Kosugi, K., Fujimoto, M., Yamakawa, Y, Masaoka, N, Itokazu, T, Mizuyama, and T, Kinoshita, A. (2013): Functional models correlating antecedent precipitation indices to bedrock groundwater levels, Journal of the Japan Society of Erosion Control Engineering, Vol.66, No.4, p.21 - 32.

How to cite: Saito, H., Katsura, S., and Tanabe, R.: Runoff dynamics and functional modeling of bedrock springs in a small granitic mountainous catchment in Hokkaido, northern Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2913, https://doi.org/10.5194/egusphere-egu26-2913, 2026.

Rainfall-induced floods and landslides are globally prevalent natural hazards. Moreover, floods and landslides often occur in a cascading manner, posing significant risks and amplifying losses beyond each individual hazard event. Effective disaster preparedness and hazard management heavily rely on sufficient knowledge of flood-landslide cascading processes and accurate assessment of potential consequences. However, existing methods predominately analyse individual hazard event, and there is a notable lack of rapid, physically-based modeling approaches, particularly for regions where observations are limited. To address this challenge, we propose a novel framework to quantify flood and landslide risks by integrating remote sensing data with a high-performance hydrological-geotechnical model. The model is driven exclusively by remote sensing data (including meteorological forcings and ground properties) and forecasts flood and landslide processes based on physical principles. Moreover, this framework quantifies risk by synthesizing hazard intensity, population exposure, and regional socioeconomic conditions, while explicitly accounting for the compound interactions between these hazards. We evaluate this framework utilizing a heavy rainfall event of July 3–4, 2012 in the Yuehe River Basin, which triggered widespread floods, landslides, and debris flows. Our results demonstrate that the model effectively reproduces meteorological forcings and disaster processes, offering a new perspective for disaster risk assessment in data-scarce regions. The proposed framework could contribute to the development of effective mitigation strategies, enhancing regional resilience against cascading natural hazards. 

How to cite: Chen, G., Chao, L., Jia, T., and Wang, S.: Coupling Remote Sensing and Hydrological-Geotechnical Modeling for Rapid Assessment of Cascading Flood-Landslide Risks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4339, https://doi.org/10.5194/egusphere-egu26-4339, 2026.

Landslide-dammed lakes are natural barriers formed by slope failures that can cause serious hazards downstream. Their stability depends on both dam shape and material and on the upstream hydrological conditions that control lake extension and water level. Changes in these conditions can increase lake level and activate hydraulic processes like seepage and overtopping, which can compromise the stability of dam. Understanding the interplay of upstream hydrology and stability is important to assess dam safety and downstream flood risk.

In early 2010, a rockslide in Attabad created a dam on the Hunza River in Pakistan, forming a lake that still exists today. In this study, lake surface area and volume were assessed using Landsat images and the Normalized Difference Water Index (NDWI), and pre-lake digital elevation model was used to estimate lake volume changes. Observations show seasonal fluctuations and consistency in lake volume over the years, influenced by spillway excavations and other hydrological processes.

A simplified geometry of dam body was defined based on literature data and images. Grain size distribution of dam materials typical of rockslides was also analyzed, and the Hazen formula was used to estimate hydraulic conductivity values. These were applied in GeoStudio SEEP/W to simulate nine scenarios with different combinations of clay and gravel permeability. Results show that total seepage (under current conditions) is moderate but strongly depends on material properties. Gravel-dominated zones have higher seepage, while clay-dominated zones have lower seepage. Some gravel areas could be prone to localized internal erosion or piping under high water levels.

We also analyze dam’s stability under different hydrological conditions. One approach is to evaluate seepage and structural response using current lake water level, which can help back-analyze and validate the mechanical properties of the dam materials. The second approach is to simulate future possible water levels to assess whether the dam remains stable under extreme conditions.

This study shows that combining remote sensing and hydrological modelling allows developing scenario-based analyses that can help understand how hydrology and dam material and shape control its stability. It provides a useful approach for monitoring and managing landslide-dammed lakes in areas with limited field data.

Keywords: Landslide dams, hydrological modeling, dam stability, scenario-based analysis, remote sensing

How to cite: Shazil, M. S., Damiano, E., and Greco, R.: Scenario-Based Assessment of Material and Hydrological Controls on Attabad Landslide Dam Stability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5859, https://doi.org/10.5194/egusphere-egu26-5859, 2026.

This study presents a methodological framework to assess climate change impacts on the hydrological conditions leading landslide occurrence. The approach is applied to a ~170 km² landslide-prone area in southern Italy, characterized by complex topography and rainfall-driven slope instability. Regional climate projections from CORDEX for the period 2006–2070, under moderate (RCP4.5) and high (RCP8.5) emission scenarios, were bias-corrected using observed rainfall data (2006–2023) and evaluated against a synthetic dataset representing present-day climatic conditions.

Aiming at event-scale detection of rainfall-triggered landslides throughout the study period, soil hydrological processes were simulated using physically based models and coupled with slope stability analyses that account for unsaturated soil behavior. Scenario-based statistical comparisons were carried out across three rainfall-homogeneous subregions. The analysis reveals a general trend toward drier conditions, in line with regional climate projections, together with enhanced rainfall variability at the subregional scale. Nevertheless, landslide occurrence is projected to increase significantly in climate change scenarios, with a more pronounced rise under RCP4.5 compared to RCP8.5.

This apparently counterintuitive response reflects contrasting changes in rainfall and landslide dynamics. Under RCP8.5, landslides are mainly triggered by more intense rainfall events, whereas under RCP4.5 they arise from the combined influence of wetter antecedent soil conditions and more intense early-peak rainfall. These results underscore the persistent and critical role of antecedent soil moisture in landslide initiation, even under rapidly evolving climate conditions.

How to cite: Roman Quintero, D. C., Greco, R., Bogaard, T., and van der Ent, R.: When Climate Change Affects Rainfall, Landslide Frequency Responds: An Assessment at the Subregional Scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5909, https://doi.org/10.5194/egusphere-egu26-5909, 2026.

Large mountainous areas of Campania (southern Italy) are frequently hit by rainfall-triggered shallow landslides, which often cause significant damage to buildings and infrastructures. Specifically, they involve steep slopes covered with unsaturated air-fall pyroclastic deposits, formed by alternating layers of ashes and pumices of variable thickness, lying upon heavily fractured limestone bedrock. The main triggering factor of these catastrophic events is the rainfall. Nonetheless, there are other causes linked to the hydrological conditions predisposing the slopes to failure (Roman Quintero et al., 2025), often associated with soil moisture conditions prior to the onset of rainfall (Greco et al., 2021). Predicting the occurrence of these landslides is highly challenging due to the significant spatial and temporal variability of the factors driving them. Thus, landslide hazard assessment needs attention and remains a critical task, especially in terms of reliably predicting the triggering location. In this work, a method for the preliminary assessment of landslide hazard in a sloping area covered by pyroclastic deposits is proposed, based on available historical precipitation records and considering only slope inclination and soil thickness as geomorphological controlling factors while assuming soil characteristics as homogeneous. The study area is located on the Cornito slope near the town of Cervinara, around 40 km northeast of the city of Naples, which belongs to the north-facing part of the Partenio Massif in the southern Apennines of Campania. Specifically, a small catchment of 0.4 km² was investigated, where on 16 December 1999 a rain event of approximately 300mm in 48h triggered several landslides evolving in the form of fast debris flows. The largest one travelled nearly 2 km downslope toward the town of Cervinara, causing destruction and killing five people. The natural landforms of the catchment were considered using the Digital Elevation Model (DEM) with a resolution of 10 m grid cell, downloaded from the dataset TINITALY/01. This DEM was obtained by simple linear interpolation of contour lines digitized from the 1:25000 maps of the Istituto Geografico Militare (IGM) before the landslides of 1999 (Tarquini et al., 2007). Grid cells were grouped into fifteen classes of slope inclination and corresponding soil thickness, ranging from 33.5° to 47.5°, for simulating the hydrological processes of rainwater infiltration. Specifically, the 1D Richards’ equation model was run to simulate soil saturation profile at hourly resolution for each cell, considering the hourly rainfall recorded during the event of 1999. The model has been calibrated with both laboratory measurements (Roman Quintero et al., 2024) and field data collected during previous hydrological monitoring activities (Marino et., 2020). Then, based on the results obtained with the unsaturated flow model, the landslide hazard map is generated by looking at cells with a Factor of Safety, calculated under the infinite slope hypothesis, smaller than 1. The generated areal landslide hazard map was validated by comparison with the documented landslide inventory, showing agreement with the spatial distribution of reported landslides, especially with the location of the scarp of the largest one recorded, with an estimated mobilized volume of 30000 m³.

How to cite: Marino, P., Abdullah, A., Roman Quintero, D. C., Santonastaso, G. F., and Greco, R.: Areal landslide hazard assessment: case study of landslide-prone area covered by pyroclastic deposits , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6463, https://doi.org/10.5194/egusphere-egu26-6463, 2026.

The development of reliable tools for assessing rainfall-induced landslide hazard over large areas is often constrained by the limited availability of historical landslide inventories and high-quality rainfall data. This challenge is particularly evident in the pyroclastic soil deposits of Campania (southern Italy), where coarse-grained soils formed by air-fallen volcanic material exist in alternating layers. These deposits are frequently affected by rainfall-induced landslides, primarily triggered by intense rainfall, with antecedent soil moisture acting as a key preparatory factor.

In this study, the Partenio and Sarno Mountains, covering an area of approximately 500 km² and monitored by 23 rain gauges, were subdivided into three zones based on the probability distributions of rainfall event series. Events were separated using a minimum inter-event time of 24 hours with rainfall amounts lower than 2 mm. The zoning reflects the orographic control on rainstorms in the area and was defined using Kolmogorov-Smirnov tests. For each zone, the NRSP stochastic model of rainfall was calibrated based on observed rainfall data, and 500-year-long synthetic hourly rainfall time series were generated. These synthetic series were then used as input to a 1D model of the flow in the unsaturated soil deposit, to simulate the response to precipitation for a representative slope in each zone. The resulting time series of soil moisture and soil suction were employed to perform slope stability analyses, evaluating the factor of safety (FS) with the infinite slope model.

Using the synthetic dataset, empirical thresholds for landslide prediction were derived for each zone, including both meteorological thresholds (based on rainfall intensity and duration) and hydrometeorological thresholds (combining rainfall depth with antecedent root-zone soil moisture). The results indicate that hydrometeorological thresholds are more effective than meteorological thresholds when rainfall and slope properties are accurately known. Moreover, the inclusion of antecedent hydrological variables allows the identification of two distinctive landslide-triggering mechanisms typical of the initial and end phases of the rainy season.

To improve the reliability of the proposed approach, uncertainties associated with the spatial variability of geomorphological slope properties and hydrometeorological variables were explicitly considered. These uncertainties were modeled as normally distributed random errors, and the synthetic datasets of the representative slopes were accordingly perturbed. Accounting for uncertainty shows the robustness of the hydrometeorological thresholds, limiting both false alarms and missed events across all zones. This result was confirmed through validation against available landslide, rainfall, and root-zone soil moisture data for the period 1999-2025.

The proposed methodology provides a practical framework for incorporating uncertainty in hydrometeorological information into landslide hazard assessment over large areas. Furthermore, once the site-specific dominant hydrological processes and controlling variables are identified, the approach can be readily transferred to other regions affected by rainfall-induced landslides.

How to cite: Abdullah, A., Roman Quintero, D. C., Marino, P., and Greco, R.: Large-scale assessment of rainfall-induced landslides in pyroclastic soils of Campania (Italy): a synthetic hydrometeorological approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8173, https://doi.org/10.5194/egusphere-egu26-8173, 2026.

Understanding the relationship between rainfall and groundwater level response is crucial for elucidating landslide mechanisms and for planning structural mitigation measures, such as groundwater drainage works, for deep-seated, slow-moving landslides. It is well known that elevated groundwater levels induce landslide displacement; however, such displacement often causes fracturing and deformation of the landslide mass. These processes can alter the internal hydrogeological structure of the landslide, potentially changing the rainfall–groundwater level response relationship. Although some previous studies have reported differences in this relationship before and after large landslide displacements, its linkage to fracturing and deformation remains unclear.

In this study, we observed groundwater level dynamics at four observation wells (depth: 1.3–7.4 m) within a deep-seated, slow-moving landslide in Biratori, Hokkaido, northern Japan. The slip surface was estimated to be located at a depth of approximately 8 m. In November 2023, the landslide experienced approximately 4 m of displacement over a two-week period. The observation period was divided into intervals before and after this large displacement, and covariance analysis was applied to evaluate changes in the rainfall–groundwater level response relationship. For each analysis period, an antecedent precipitation index (API) was calculated from daily rainfall data. The half-life (days) and lag time (days) were optimized to maximize the correlation coefficient with the observed groundwater levels, and these optimized parameters were used in the covariance analysis. Preliminary results indicate statistically significant changes at three of the four observation wells. Furthermore, comparison with topographic changes derived from UAV-LiDAR measurements (10-cm resolution DEMs acquired on November 9 and 28, 2023), suggests that changes in half-life reflect variations in landslide-mass permeability caused by compression and tension, whereas decreases in lag time indicate the formation of new seepage pathways associated with increased fracturing. These findings suggest that the rainfall–groundwater level response relationship is not stable in actively moving landslide masses. Further analyses will examine and discuss its linkage to topographic changes in greater detail.

How to cite: Kobayashi, S., Katsura, S., Aoki, T., Kasai, M., and Hayakawa, Y.: Changes in rainfall–groundwater level response associated with large displacement of a deep-seated landslide in Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8752, https://doi.org/10.5194/egusphere-egu26-8752, 2026.

Hydrometeorological thresholds are central to many operational landslide early warning systems, yet they often remain coarse and weakly linked to slope physics. Two persistent limitations are (i) the dependence on landslide inventories that are incomplete and often poorly timed, and (ii) the assumption that a single regional threshold can represent heterogeneous and evolving slope stability conditions. This contribution presents a deformation-informed perspective to advance threshold-based landslide early warning. We show that slope deformation, measured as continuous time series, can act as an transition state variable that bridges hydrometeorological forcing and slope failure. By explicitly incorporating deformation, hydrometeorological thresholds can be better constrained as well as better used operationally. First, deformation observations can be used to supplement event information for threshold assessment. Automated extraction of “deformation events” from geodetic time series can complement landslide records as physically meaningful proxies, reducing the sensitivity of threshold estimation to inventory incompleteness and timing uncertainty, and improving the robustness of calibrated thresholds. Second, deformation can guide the spatiotemporal refinement of warning criteria. By quantifying how different slopes respond to rainfall over multiple time windows, deformation-derived indices can characterize slope-specific response patterns and stability states. This information enables a downscaling strategy in which regional hydrometeorological thresholds for landslide initiation are transformed into slope-specific, dynamically updated thresholds that better reflect local conditions and temporal changes in stability. In this way, deformation moves thresholds from static and regionally averaged triggers toward adaptive criteria that are more physically grounded and spatially actionable.

Overall, the proposed deformation-aware framework brings two complementary benefits in early warning: (1) strengthening landslide initiation threshold development through deformation-informed event characterization, and (2) enhancing threshold application through slope-specific, time-varying adaptation. This approach is sensor-agnostic (applicable to GNSS and InSAR) and compatible with different threshold formulations, offering a practical pathway to improve reliability and reduce uncertainty in landslide early warning across data-limited and highly heterogeneous regions.

How to cite: Feng, X., Lombardo, L., Du, J., Chai, B., and Bogaard, T.: Deformation-informed hydrometeorological thresholds for landslide early warning: inventory enhancement and spatiotemporal downscaling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15316, https://doi.org/10.5194/egusphere-egu26-15316, 2026.

Soil moisture plays a central role in slope hydrology by integrating atmospheric forcing and subsurface processes, thereby shaping antecedent wetness conditions relevant to landslide preconditioning. A key property governing this role is soil moisture memory, defined as the persistence of moisture anomalies following external perturbations. Despite its importance, the seasonal organization of soil moisture memory and its physical controls remain insufficiently constrained at regional scales.

This study focuses on understanding how soil moisture variability across Italy is organized by the interplay between external hydro-meteorological forcing and internal system persistence across seasons. Specifically, using high-resolution gridded reanalysis-based data within a causal discovery framework, the analysis examines the seasonal dominance of different drivers and the spatial and temporal variability of soil moisture persistence, with emphasis on large-scale background hydrological states.

The results indicate pronounced spatial and seasonal heterogeneity. Soil moisture variability is primarily governed by atmospheric water inputs over large portions of the domain, while cryospheric and energy-related processes become relevant under specific climatic and seasonal conditions. Crucially, soil moisture persistence exhibits systematic seasonal contrasts and is not uniformly associated with the apparent strength of external forcing. The joint behavior of forcing strength and memory instead organizes soil moisture dynamics into distinct seasonal regimes, reflecting different modes of system response shaped by land–atmosphere coupling and soil water loss processes. These findings support a physically consistent interpretation of antecedent wetness conditions relevant to landslide preconditioning.

How to cite: Liu, X. and De Michele, C.: Seasonal controls and memory of soil moisture variability across Italy: a process-oriented perspective relevant to landslide preconditioning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11992, https://doi.org/10.5194/egusphere-egu26-11992, 2026.